No, we can never tell that correlation is equal to causation. It is a common misconception when looking at data tables.
For instance, if we have a table that shows a correlation of scores on two tests in a row. We may see that there is a correlation between the two numbers. Students who did well on the first test may have done well on the second as well, and those who have not done well on the first may have done poorly on the second.
However, doing bad on the first test does not cause someone to do bad on the second one. Likely the cause is their studying habits, intelligence or aptitude in the area.
So in this example, there is correlation between the sets of data, but it does not prove causation.
Answer: 92,000
Step-by-step explanation: It wants us to round to the nearest thousand, so how we do that is we look at the digit one over from the thousands place (the hundreds place) if that is 5 or more we would round the thousands place up if it is a 4 or less we leave the thousands place the same but replace all the smaller places with 0, that then leads us to our answer 92,000
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The number of guests that were at the party is 26. This was illustrated in the probability.
<h3>How to compute the probability?</h3>
From the information, it was stated that twenty six soft-drinks were consumed. Therefore, it implies that there were 26 people.
From the information, 9 guests had raspberry and 13 had cola while the remaining was shared equally. Therefore, the raspberry consumed will be 11 and 15 cola were drank.
The probability that a randomly chosen party guest had a cola drink will be:
= 15/26
The probability that a randomly chosen party guest had a raspberry drink will be:
= 11/26
The probability of the people who had both a raspberry and cola drink will be 0 since everyone had different drinks.
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Answer: 30x-18
Step-by-step explanation:
6 times 5 = 30
6 times 3 = 18
Answer is 30x-18
Theo has $51. Capri has $17. Leo has $7. $51+$17+$7=$75.